Machine Learning Algorithms
Description: The discipline aims to study the working principles, architecture, and applications of various algorithms that allow computational resources to learn from data and perform tasks without explicit programming. The course covers a wide range of topics, from fundamental algorithms such as linear regression, logistic regression, k-nearest neighbors and decision trees, to more complex models such as neural networks, machine learning with a teacher, machine learning without a teacher and deep learning. The course will enable undergraduates to develop an understanding of different types of machine learning algorithms and apply suitable algorithms to solve different problems. The course will cover implementing machine learning algorithms; tuning algorithm parameters; training and testing models; visualizing results; and designing and implementing machine learning systems to solve real-world problems. Upon completion of the course, the learner will have a thorough understanding of the concepts and practical skills to develop successful machine learning systems.
Amount of credits: 5
Пререквизиты:
- Computer Modeling
Course Workload:
| Types of classes | hours |
|---|---|
| Lectures | 15 |
| Practical works | |
| Laboratory works | 30 |
| SAWTG (Student Autonomous Work under Teacher Guidance) | 30 |
| SAW (Student autonomous work) | 75 |
| Form of final control | Exam |
| Final assessment method |
Component: Component by selection
Cycle: Base disciplines
